Optimization tool for auditory devices

ABSTRACT

A system for controlling parameter settings of an auditory device includes: an auditory device processor; an auditory device output mechanism including modifiable parameter settings; an auditory input sensor that detects an environmental sound; a database in communication with the auditory device processor, the database pairing each of a plurality of sets of parameter settings with a corresponding sound profile; a memory in communication with the processor and including instructions that, when executed by the processor, cause the processor to: receive the environmental sound detected by the auditory input sensor; analyze a frequency content of the environmental sound; compare the frequency content of the environmental sound with the sound profiles stored in the database and, in response to the comparison, select one of the sound profiles; and automatically adjust the parameter settings of the auditory device output mechanism to match the set of parameter settings associated with the selected sound profile.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application incorporates by reference and claims the benefit ofpriority to U.S. Provisional Application No. 62/430,062, filed on Dec.5, 2016.

BACKGROUND OF THE INVENTION

The present invention relates systems and methods for optimizingparameters of hardware for audiological devices. More specifically, thepresent invention relates to systems and methods in which acoustic wavesare transformed into electrical signals in a device, and the settings ofthe device are tailored to the individual.

Programming hardware for audio signals is complicated due to thecomplexity of audio signals. In addition to the basic problemsassociated with reproducing a constantly changing sound comprised of anoverlapping collection of various pitches and amplitudes, problems arecompounded by signal to noise issues, threshold hearing variances acrossa wide range of the spectrum in which humans can hear, and other uniquefactors. With such a complex variable set, or from another perspective,such a wide optimization space, it is difficult for a user or operatorto arrive at an optimized setting.

For example, cochlear implants include technology that transformscomplex auditory waves into pulses to be sent to a plurality of channelson the inner cochlea of a patient in order to stimulate the neurons onthe select channels. The process of transforming auditory waves intoelectronic signals requires the transformation of a multitude ofinformation including frequency, amplitude, and voltage among backgroundnoise and environments into an electrical signal to recreate hearing.

Cochlear implants are neural prostheses that help severely-to-profoundlydeaf people to restore some hearing. Physically, three components can beidentified: the speech processor with its transmission coil, thereceiver and stimulator, and the cochlear implant electrode array. Thespeech processor receives sound from one or more microphones andconverts the sound into a corresponding electrical signal. While thehearing range of a young healthy human is typically between 0.02 and 20kHz, it has been assumed for coding of acoustic information in cochlearimplants that most of the information used for communication is in thefrequency range between 0.1 and 8 kHz. The frequency band from 0.1 to 8kHz is divided into many smaller frequency bands of about 0.5 octaveswidth. The number of small frequency bands is determined by the numberof electrodes along the electrode array, which is inserted into thecochlea. Each frequency band is then treated by a mathematicalalgorithm, such as a Hilbert transform that extracts the envelope of thefiltered waveform. The envelope is then transmitted via an ultrahighfrequency (UHF) connection across the skin to a receiver coil, which wassurgically implanted behind the ear. The envelope is used to modulate atrain of pulses with a fixed pulse repetition rate. For each of theelectrodes, a train of pulses with fixed frequency and fixed phase isused to stimulate the cochlear nerve. Multiple algorithms have beenimplemented to select a group of 4-8 electrode contacts for simultaneousstimulation.

Damage of cochlear neural structures can result in severe deafness.Depending on the neural degeneration in the cochlea performance, theperformance of a cochlear implant user may vary. Changes that occurinclude the demyelination and degeneration of dendrites and neuronaldeath. The neuronal loss can be non-uniform and results in “holes” ofneurons along the cochlea. Holes lead to distortion of the frequencymaps, which affects speech recognition. Caused by changes in myelinationand synapse size, changes in firing properties of the nerve weredescribed such as prolonged delay times and changed refractory periods.In the brainstem and midbrain the neuronal connections appear to remainintact. However, a decrease in neuron size, afferent input, synapse sizeand density can be detected. Neural recordings reveal a change inresponse properties that adversely affect temporal resolution such aselevated thresholds, de-synchronization, increased levels of neuraladaptation, increased response latencies. A loss of inhibitoryinfluences has been described. At the cortex, spatially larger corticalactivation was seen with (PET). The findings support a plasticreorganization and more intense use of present auditory networks.

A conventional cochlear implant includes a speech processor thattransforms the acoustic waves received on the microphone into anelectrical signal that stimulate the implanted electrode array, andconsequently, the auditory nerves. Auditory waves are a complexsummation of many different wave forms, and the processor decomposes thecomplex auditory signal received on the microphone into discretecomponent frequencies or electrical pulses to be sent to the auditoryneurons through the electrodes. Nerve fibers in the vicinity of theelectrodes are stimulated and relay information to the brain. Loudsounds produce high-amplitude electrical pulses that stimulate a greaternumber of nerve fibers, while quiet sounds produce low-amplitude pulseseffected a lesser number of nerve fibers. Different variables within thesoftware on the processor affect the output of the cochlear implantspeech processor.

To activate the cochlear implant, an audiologist tunes the levels andstimulation parameters of the speech processor so that the sounds pickedup by the microphone are heard at the individual's ideal loudness level.Initially, the audiologist stimulates the implant's channels orelectrode pairs with small electrical pulses to test whether the userhears the stimulus. Over the course of subsequent sessions, theaudiologist performs a series of tests to understand the user'slistening needs. The user's cochlea is tuned to perceive differentpitches depending on the area being stimulated. During the sessions, theaudiologist stimulate the implant's channels to simulate pitchdifferences. The audiologist will also vary the electrical current oneach channel to find the most comfortable loudness level. Theaudiologist may also take threshold measurements to understand theuser's softest level audible on each channel. The audiologist ultimatelygenerates a map that is downloaded to the speech processor to enable theprocessor to appropriately adjust volume levels based on theindividual's needs.

With cochlear implants and other hearing devices, each patient isunique. Following implantation, changes occur that can affectperformance of the device. Changes include genetic disorders, iatrogenicprocedures, ototoxic drugs, or loud noise exposure. The user's hearingwill change over time, requiring additional visits to the audiologist inorder to rerun the tests and adjust the map accordingly.

Additionally, hearing devices other than cochlear implants, such asrecent hearing aid technology, may require programming based onaudiological feedback during testing to achieve optimal results. Thelatest generations of hearing aids and other “hearables” includeparameter settings for amplification, compression, noiserejection/cancellation, etc. Being able to fine tune each of theparameters, in each ear, provides even greater flexibility in theoptimization of these devices. However, the complexity created by themany parameters can be a challenge for manual tuning.

Accordingly, there is a need for an optimization system for effectivelyadjusting a large number of parameters of a hearing device whileaccounting for a variety of hearing situations.

SUMMARY OF THE INVENTION

The optimization system of the present application allows a patient tointuitively define parameter settings for a large number of parametersin association with a variety of environments. The optimization systemincludes a first module, a second module, and a third module. It isunderstood that the first through third modules may be integrated into asingle program or be provided in a fewer or greater number of modules.The first module collects and analyzes a wide range of patient feedbackas input data to determine ranges for each parameter tailored to thecochlea of the patient. The second module includes a plurality of userinterfaces that prompt the patient to select a preferred set of datapoint(s) corresponding to a specific pitch and/or frequency. The datapoints initially provided to the patient in the second module are withinthe specific ranges provided as output from the first module. Within thesecond module, one or more user interfaces allow the patient to comparea large number of parameter settings against one another in a singletrial.

The third module includes a database on which a reference bank of soundsor environments that identify frequency content associated with eachacoustic environment is stored. Using wavelet scattering transforms, aclassifier algorithm determines the frequency content of each acousticenvironment. Alternative methods such as traditional Fourier transformsor spectrograms may be used. Support vector machine (SVM), K-clusteringmechanisms, or any type of clustering methods is used to group theinformation contained in each acoustic environment to create a referencebank of sounds (i.e., noisy restaurant, beach, and metro station).

In one embodiment, a system for controlling parameter settings of anauditory device includes: an auditory device processor; an auditorydevice output mechanism controlled by the auditory device processor, theauditory device output mechanism including one or more modifiableparameter settings; an auditory input sensor that detects anenvironmental sound and communicates with the auditory device processor;a database in communication with the auditory device processor, thedatabase pairing each of a plurality of sets of parameter settings witha corresponding sound profile; a memory in communication with theprocessor and including instructions that, when executed by theprocessor, cause the processor to: receive the environmental sounddetected by the auditory input sensor; analyze a frequency content ofthe environmental sound; compare the frequency content of theenvironmental sound with the sound profiles stored in the database and,in response to the comparison, select one of the sound profiles; andautomatically adjust the parameter settings of the auditory deviceoutput mechanism to match the set of parameter settings associated withthe selected sound profile.

In some embodiments, when the auditory device processor analyzes afrequency content of the environmental sound it uses a waveletscattering transform to analyze the frequency content of theenvironmental sound. In other embodiments, when the auditory deviceprocessor analyzes a frequency content of the environmental sound ituses a Fourier transform to compute the frequency content of theenvironmental sound.

In some embodiments of the systems herein, the sound profiles areclustered by similarities. The frequency content of the environmentalsound may include one or more properties selected from the groupcomprising a signal-to-noise ratio, an amplitude range, and a pitchrange. The one or more properties selected from the group comprising asignal-to-noise ratio, an amplitude range, and a pitch range of thefrequency content of the environmental sound matches the correspondingone or more properties selected from the group comprising asignal-to-noise ratio, an amplitude range, and a pitch range of one ofthe sound profiles.

In some examples of the system, the auditory device output mechanism isan electrode of a cochlear implant and the auditory input sensor ismicrophone of a cochlear implant.

In some examples of the system, the auditory device output mechanism isspeaker of a hearing aid and the auditory input sensor is microphone ofa hearing aid.

Each set of the plurality of sets of parameter settings may includeamplification settings, compression settings, and directional noiserejection settings.

In some examples of the system, each sound profile is associated with astored geolocation, the system further comprises a location sensingmechanism in communication with the auditory device processor, and whenthe processor compares the frequency content of the environmental soundwith the sound profiles stored in the database and, in response to thecomparison, selects one of the sound profiles, the processor furthercompares a present geolocation of the auditory device output mechanismidentified by the location sensing mechanism with the storedgeolocations.

An objective of the present design is to provide a user-friendlyoptimization system for adjusting a variety of parameters of a hearingdevice. In some examples, the hearing device is a cochlear implant. Inother examples, the hearing device is a hearing aid. In other examples,the hearing device is another hearable device.

An objective is to provide a system for automatically controllingparameter settings of an auditory device such that the deviceautomatically updates its settings in response to recognizing theauditory environment in which it is being used.

An objective is to improve the performance of auditory devices across awide range of audio environments by enabling real-time adaptation of thesetting of the device.

Additional objects, advantages and novel features of the examples willbe set forth in part in the description which follows, and in part willbecome apparent to those skilled in the art upon examination of thefollowing description and the accompanying drawings or may be learned byproduction or operation of the examples. The objects and advantages ofthe concepts may be realized and attained by means of the methodologies,instrumentalities and combinations particularly pointed out in theappended claims.

BRIEF DESCRIPTION OF THE DRAWINGS

The foregoing and other objects, features, and advantages of the presentdisclosure set forth herein will be apparent from the followingdescription of particular embodiments of those inventive concepts, asillustrated in the accompanying drawings. Also, in the drawings the likereference characters refer to the same parts throughout the differentviews. The drawings depict only typical embodiments of the presentdisclosure and, therefore, are not to be considered limiting in scope.

FIG. 1 is a schematic illustrating the components of the optimizationsystem of the present disclosure in use with a cochlear implant.

FIG. 2 is a block diagram illustrating the interaction between thefirst, second, and third modules of the optimization system of FIG. 1.

FIGS. 3A-3E are charts illustrating the potential variance provided bymodifying the parameters of an example coding strategy of a cochlearimplant that may be used with the optimization system of FIG. 1.

FIG. 4 is a representation of a user interface of an audiometrygathering screen.

FIG. 5 is a representation of a first user interface of a sound testingscreen.

FIG. 6 is a representation of a second user interface of a sound testingscreen.

FIG. 7 is a representation of a rating spectrum used in connection withthe second user interface of FIG. 6.

FIG. 8 is a representation of a third user interface of a sound testingscreen.

FIG. 9 is a graphic representation of a cluster of sounds of a referencebank.

DETAILED DESCRIPTION

The present application provides an optimization system that optimizesthe parameters of an auditory device based on the individual's specificneeds to improve the user's ability to hear.

FIG. 1 is a block diagram illustrating an example system 100 forperforming various activities involving the activation, modulation,and/or blockage of neurons within the brain using an audio device suchas a cochlear implant or hearing aid. As illustrated, the system 100includes a neurostimulation device (NSD) 102. The NSD 102 may beimplantable (e.g., below the skin), or alternatively, may be some typeof external device, such as a cochlear implant device or a hearing aiddevice. Primary example used herein is a cochlear implant, although thesystem 100 may apply to a hearing aid or other hearable device.

In the example shown in FIG. 1, the NSD 102 transforms acoustic wavesinto electrical impulses. An auditory input sensor 104 such as amicrophone on the NSD 102 captures the acoustic wave, and a controller106 including an auditory device processor 108 deconstructs the acousticwave and utilizes a pulse generator 110 to generate discrete electricalpulses that are then provided to an auditory device output mechanismsuch as an electrode array on the cochlea. Specifically, the pulsegenerator 110 generates electrical impulses (“pulses”) in specificpatterns for electrical stimulation of nervous tissue of the cochlea.Stated differently, the pulses generated by the pulse generator 110 areapplied in specific patterns to specific regions and/or portions of thenervous system to deliver neurostimulation. The pulse generator 110 maybe electrically coupled to electrodes 112 and 114 via one or more leads116 and 118, respectively, and thereby provide neurostimulation to thespecific regions of the nervous system. The pulses generated by thepulse generator 110 are conducted through the one or more leads 116 and118 and terminated in the electrodes 112 and 114 generally implanted inthe tissue of the nervous system. In another embodiment where theauditory device is a hearing aid, the auditory device output mechanismmay be a speaker.

The auditory device processor 108 on the controller 106 controls thepulse generator 110 to deliver electrical pulses (i.e.,neurostimulation) according to a selected stimulation parameter set(e.g., pulse amplitude, pulse width, pulse frequency, etc.) and/or otherinstructions to applicable regions of the nervous system.Neurostimulation programs or coding strategies based on variableparameters that are used in the delivery of neurostimulation therapy(i.e., stimulation) may be stored in a memory 120, in the form ofexecutable instructions and/or software, for execution by the auditorydevice processor 108. The auditory device or NSD 102 may also include aglobal positioning system (GPS) chip 121 and a database of referencesound profiles 123, which may be utilized in the programming stored onthe memory 120 as described below.

In some embodiments, the controller 106 may contain a machine-learninglogic unit (“MLU”) 122 that is trained to perform machine-learningoperations involving the generation of various predictions that may beused to optimize the functionality of the NDS 102 and/or initiate andoptimize neurostimulation therapy provided to a patient via the NDS 102.The MLU 122 may process data received from users interacting with theNDS 102 when generating such predictions. Although the controller 106 isillustrated as being included within the NSD 102, in some embodiments,it may be implemented in a computing device that is separate from theNDS 102. In such an embodiment, the controller 106 may communicate withthe NSD 102 remotely, such as through a communications network 124,which may be a telecommunications network, the Internet, an intranet, alocal area network, a wireless local network, radio frequencycommunications protocol, or any other type of communications network, aswell as combinations of networks.

The NSD 102 may be communicatively connected to an optimization device126 and/or an audiologist device 128 locally or via the communicationsnetwork 124 to receive input that may be processed to optimizeneurostimulation therapies and/or optimal functions of the NDS 102. Eachof the optimization device 126 and the audiologist device 128 providesuser interface(s) that enable a patient or user to provide the input(e.g., data) to the NSD 102 that defines, qualifies, and/or quantifiesaspects of the neurostimulation therapy provided by the NDS 102. Morespecifically, variables of the equations that are part of computerprogram stored in the memory 120 of the NSD 102 are set by theoptimization interface and/or the audiologist interface of theoptimization device 126 and the audiologist device 128, respectively.Each of the devices 126, 128 may include a processor-based platform thatoperates on an operating system, such as Microsoft® Windows®, Linux®,i0S®, Android®, and/or the like that is capable of executing and/orotherwise generating the interfaces.

The user or operator of the optimization device 126 works with thepatient wearing the NSD 102 to gather user feedback in response to audiotests as shown in FIGS. 4-8 and described below. The user interfaces ofthe optimization device 126 can be coupled to memory 130 that can storeprogram instructions 132 to run the optimization system. Further, thememory 130 may also store communication instructions 134 to facilitatecommunicating with one or more additional devices, one or morecomputers, and/or one or more servers. The memory 138 may includegraphical user interface instructions 136 to facilitate graphic userinterface processing.

The audiologist operates the device 128 to directly adjust theprogramming or instructions on the memory 120 of the NSD 102.Specifically, the audiologist may provide input in the form of a set ofstimulation parameters that define various parameters, such as pulseamplitude, pulse width, pulse frequency, etc., any of which may be usedto automatically determine a specific neurostimulation therapy (e.g.,parameter space) for a particular patient. Based on such input, thecontroller 106 logically directs the pulse generator 110 to modifyinternal parameters and vary the characteristics of stimulation pulsestransmitted to the nervous system. The audiologist may interact with theoptimization device 126 to provide feedback regarding the success of thesimulation (e.g., better, same, or worse) in comparison to previousneurostimulation therapies, to modify parameters of the currentsimulation, etc.

Each of the above identified instructions and applications cancorrespond to a set of instructions for performing one or more functionsdescribed herein. These instructions need not be implemented as separatesoftware programs, procedures, or modules. The memory 130 can includeadditional instructions or fewer instructions. Furthermore, variousfunctions of the system 100 may be implemented in hardware and/or insoftware, including in one or more signal processing and/or applicationspecific integrated circuits.

In one example, the memory 120 includes stored instructions that, whenexecuted by the auditory device processor 108, cause it to deconstructacoustic waves into discrete electrical signals and to generateelectrical pulses through the pulse generator. In one example, U.S. Pat.No. 9,717,901 discloses a frequency-modulated phase coding (FMPC)strategy to encode acoustical information in a cochlear implant 102. Theentirety of the disclosure provided by U.S. Pat. No. 9,717,901 isincorporated herein. The FMPC strategy utilizes the following equationthat describes the relationship between the sound level at the outer earcanal and the corresponding rate of action potentials that can berecorded from a single auditory nerve fiber. This function is expressedbelow and includes cochlear nonlinearities and depends on five criticalparameters: a spontaneous rate (a₀), a maximum rate (a₁), a thresholdfor stimulation (a₂), a level for nonlinear behavior (a₃), and a valuedescribing the slope after the level for nonlinear behavior (a₄).

${R = {a_{0} + \frac{a_{1}*d^{2}}{a_{2}^{2} + d^{2}}}},$

where R is the mean discharge rate, and d is

$d = \left\lbrack \frac{a_{3}^{({\frac{1}{a_{4}} - 1})}*p^{\frac{1}{a_{4}}}}{a_{3}^{({\frac{1}{a_{4}} - 1})} + p^{({\frac{1}{a_{4}} - 1})}} \right\rbrack^{a_{4}}$

where the variables denote the following:

-   -   a₀=the spontaneous discharge rate of the primary afferent,    -   a₁=the maximum increase of the discharge rate,    -   a₂=the sound pressure of the half maximum discharge rate,    -   a₃=the sound pressure at which nonlinear behavior occurs,    -   a₄=the exponent of the power-law slope in the nonlinear region,        p the sound pressure level at the tympanic membrane, and

p=10*log 10(abs(S1(frequency))), where S1 is the Short Time FourierTransform (STFT) of the acoustic signal.

Each of FIGS. 3A-3E illustrates the mean discharge rate R having variousvalues of the parameters a₀, a₁, a₂, a₃, a₄. Values of each parameterper graph of FIGS. 3A-3E are provided in Table 1 below.

TABLE 1 Parameter values for FIGS. 3A-3E a₀ a₁ a₂ a₃ a₄ FIG. 3A 0:0.1:11 20 50 0.5 FIG. 3B 0 0:0.1:1 20 50 0.5 FIG. 3C 0 1 5:5:50 50 0.5 FIG.3D 0 1 20 20:10:120 0.5 FIG. 3E 0 1 20 50 0.1:0.1:1

Traces in FIG. 3A show that the spontaneous discharge rate a₀ shifts thecurve towards larger values. The maximum rate a₁ limits the maximum rateto the number selected (FIG. 3B). The level for threshold a₂ has largeeffects on the mapping. Low threshold values result in a fast increasein the rate and quick saturation whereas large threshold values slow theincrease in rate but limit the maximum in achievable rate (FIG. 3C).Smaller effects are seen from the parameters a₃ and a₄ (FIGS. 3D and3E). Default values are selected (a₀=0, a₁=1; a₂=20; a₃=50, and a₄=0.5),which must be adjusted individually during later sessions with the CIuser.

The above variables are examples of the types parameters that areadjusted during the audiologist tuning sessions. Any hearing device canhave more or fewer parameters noted above depending on the codingstrategy.

In the systems of the present application, the optimization system 200is used to optimize the values of the parameters of the coding strategyprogrammed on the memory 120 of the NSD 102. In the primary exampleprovided, the optimization system 200 is described as being embodied infirst, second, and third modules 202, 204, 206. It is understood thatany one or more of the three modules 202, 204, 206 can be usedindependently or in any combination to describe the features andfunctions described herein. It is also understood that all three modules202, 204, 206 could be a single system, independent systems, orcombinations thereof.

Referring to FIG. 2, the first module 202 collects and analyzes a widerange of patient feedback 208 as input data to determine ranges for eachparameter tailored to the cochlea of the patient. The memory 130 on theoptimization device 126 includes stored instructions that, whenexecuted, cause it to prompt the patient to identify threshold decibellevels 210 under a plurality of conditions. In one embodiment, theplurality of conditions includes first through fifth conditionsdescribed below, although any number and/or types of conditions may beused to effectuate the desired thresholds.

The first condition determines the patient's threshold for detectingspeech. A sound is provided to the patient and gradually increases involume. The patient indicates when he or she first detects the noiseagainst a quiet background.

The second condition determines the patient's preference for the mostcomfortable decibel level. A sound bite of speech is provided to thepatient and gradually increases in volume. The patient indicates when heor she first understands the speech clearly at a comfortable level, suchas listening to an audiobook.

The third condition determines the patient's threshold for recognizingspeech. A sound bite of speech is provided to the patent at a highdecibel level and gradually decreases in volume. The patient indicateswhen he or she can no longer understand what is being said.

The fourth condition determines the patient's threshold for the mostuncomfortable decibel level. A sound bite of speech is provided to thepatient and gradually increases in volume. The patient indicates whenthe speech reaches a level that it is uncomfortable to hear.

The fifth condition determines the patient's threshold for understandingspeech while raising the signal to noise ratio. A sound bite of speechis played as the background noise is gradually increased (or the SNR isgradually decreased). The patient indicates when the speech is no longerrecognizable due to the background noise.

The GUI instructions 132 on the memory 130 of the optimization device126 provide algorithmic processing that compares the patient's thresholdlevels 210 for each of the five conditions with the threshold levels fornormal hearing listeners. The average levels of a normal hearinglistener are based on a database of audiological waves representingspeech having a variety of pitches and frequencies against variouslevels of background noise. If the threshold levels 210 are outside ofan acceptable range for each condition, the patient is deemed hearingimpaired. An output 212 of the first module 202 is a plurality of rangesof decibel levels the patient has indicated as being at an acceptablelevel or within an acceptable range per condition.

FIG. 4 provides an example user interface 300 for collecting audiometrydata 208, 210 in the first module 202. By striking the play button 302,the patient triggers the system to provide a sound. The user interface300 includes arrows 304 that the patient can select to modify thevolume. When testing other conditions, the arrows 304 may correspond tovariables other than volume as desired or necessitated by the testingcondition. The patient clicks on a button 306 labeled “accept” toidentify the decibel level that corresponds to the patient's preferencebased on the conditions above.

The first module 202 may be tailored to test for specific aspects of thecochlear implant NSD 102. For example, the threshold levels for thevarious conditions are tested for an auditory wave that is a complexsummation of many different wave forms that affect a plurality ofchannels of the electrode array. In some embodiments, the electrodearray of the cochlear implant is tested as a collective. In otherembodiments, the conditions are tested separately for each channel.

The second module 204 includes a plurality of user interfaces 500, 600,700 of FIGS. 5-8, respectively, that prompt the patient to select apreferred set of data point(s) corresponding to a specific pitch and/orfrequency. The data points initially provided to the patient in thesecond module 204 are chosen by the second module 204 to be within thespecific ranges provided as output 212 from the first module 202. Withinthe second module 204, one or more user interfaces allow the patient tocompare a large number of parameter settings against one another.Through the use of the second module 204, the patient is presented withat least two sound options and asked to select the preferred option. Inresponse to receiving the user's preferred option, the system generatessubsequent sound options for testing. The system generates subsequentsound options based on user feedback related to the previous soundoptions. In a preferred embodiment, statistical analysis of theparameter space enables the system to select subsequent options that aremost likely to provide the most meaningful feedback to the system tooptimize the efficiency of the iterative selection process. For example,the statistical analysis may include the use of a Gaussian function.Accordingly, the system can automatically explore areas of the parameterspace that statistically will provide the most useful information, whichresults in the most efficient (though not necessarily straight line)path to optimal settings.

In the first embodiment shown in FIG. 5, the user interface 400 includesan area 402 where each dimension 404, 406 corresponds to a parameterbelonging to a function that alters properties of each auditory filtersimultaneously. For example, in the embodiment shown in FIG. 5, theparameters associated with the x- and y-axes 404, 406 may beamplification and noise cancellation, respectfully. To begin the tuningprocess, a user selects a point within the two-dimensional framework,area 402. The parameters are adapted to reflect the settingscorresponding to the selected point and a sound is presented to theuser. The user then selects another point within the area 402 and theparameters are updated and a further sound is presented to the userusing the updated parameters. The user continues selecting points in thearea 402, typically clustering within a zone within the area 402, untilthe user indicates a preferred setting by selecting the “accept” button408. Once a patient selects a specific space within the area thatreflects the preferred sound, that point is accepted and the task isreset with the new word and/or new parameters assigned to the x- andy-axes 404, 406 being appointed. After a plurality of trials have beencompleted, the points that were accepted are used to compute a bestestimate for the set of parameters being optimized and the parameters ofthe tested NDS 102 are updated accordingly.

In a second embodiment shown in FIGS. 6 and 7, the user interface 500involves the implementation of an interactive genetic algorithm (IGA) todetermine ideal cochlear implant settings. Genetic algorithms use thebiological metaphor of evolution and natural selection to construct aset of rules for searching a parameter space for optimal solutions.Genetic algorithms are valued for their flexibility and robustness tolocal minima due to the high amount of stochasticity utilized during thesearch process.

In user interfaces 500, 600 the search is initiated by presenting thepatient with a small number of device parameters which he or she isasked to rate on a scale relative to each other. In one embodiment,about half of these initial parameters are drawn randomly and uniformlyfrom the parameter space while the other half are drawn at random withina parameter space closely related to the original device settings of thecochlear implant user. The relative ratings for each parameter are thenused as inputs for a fitness function which determines which of thesettings should be ‘selected’ to be recombined with other survivingparameters to create ‘child’ parameters that will then undergo the samepruning and recombination procedure in the next generation. Theseiterations proceed for about 15-20 generations at which point themajority of the recommendations made are appealing to the user.

For example, in the embodiment 500 shown in FIGS. 6 and 7, the patientis instructed to comply with an instruction presented in the message box502 of the user interface 500. In the illustrated example, the messagebox 502 instructs the patient to “Use the panel on the right to indicatehow good or bad the current setting sounds.” The patient presses theplay button 504, which triggers a first sound to be presented to thepatient. The patient then selects either of the “good” button 506 or the“bad” button 508 provided adjacent to the message box 502 to provide arating that corresponds to a point on a rating spectrum 510 shown inFIG. 7. In one example, the length of time that the button 506, 508 isselected corresponds to how strongly the sound is rated. For example,selecting the “good” button 506 for a single click corresponds to apoint on the spectrum 510 just to the right of the center 512, whileselecting the “good” button 506 for a longer period of time causes therating to be closer to the “good” end 514 of the spectrum 510. After aplurality of trials have been completed, the ratings provided by thepatient are used to compute a best estimate for the set of parametersbeing optimized and the parameters of the tested NDS 102 are updatedaccordingly.

In the third embodiment shown in FIG. 8, the user interface 600 is basedon a machine learning framework known as the “dueling bandits” problem.Duels are defined as random comparisons between pairs of parameterswhere the user determines the ‘winner’ of each duel. In the userinterface 600, two sets of device parameters are drawn at random fromthe parameter space and played in sequence. The user then selects whichof the two settings he or she liked more by pressing a left button 602or a right button 604 on the user interface 600, with an additionalbutton 606 to repeat the stimuli or ignore them if they both soundunacceptable or similar. The model works under the assumption that theparameters corresponding to the winners of these duels will, on average,be informative in defining a function for recommending sets ofparameters that have the highest probability of winning a duel against aparameter generated at random from the parameter space.

The second module 204, and the one or more user interfaces 500, 600, 700employed, provide specific parameter settings 222 associated withspecific sounds or environments.

Referring back to FIG. 2, the third module 206 includes a database 224on which a reference bank of sounds or environments that identifyfrequency content associated with each acoustic environment is stored.Using wavelet scattering transforms, a classifier algorithm determinesthe frequency content of each acoustic environment or sound profile.Alternative methods such as traditional Fourier transforms orspectrograms may be used. Support vector machine (SVM), K-clusteringmechanisms, or any type of clustering methods is used to group theinformation contained in each acoustic environment to create a referencebank of scenarios (i.e., noisy restaurant, commuter train, office,living room, etc.).

Optimized parameter settings 222 associated with specific environmentsthat are output from the second module 204 are provided as input to thethird module 206. The optimized parameter settings 222 are matched toclusters within the reference bank of sounds in order to associate theparameter settings with a greater range of environments. Simultaneously,the acoustic environment received on the microphone or auditory inputsensor of the cochlea implant or other hearable device is compared withthe reference bank of sounds to identify a comparable environment havingassociated parameter settings. The associated parameter settings 230 areoutput to the memory of the cochlear implant and automatically factoredinto the coding strategy of the cochlear implant.

The third module controls the parameter settings of the auditory deviceor the NSD. In one embodiment, the auditory device 102 includes anauditory device processor 106, an auditory device output mechanismincluding one or more modifiable parameter settings, and an auditoryinput sensor 104 that detects an environmental sound and communicateswith the auditory device processor 108. The auditory device outputmechanism is any output mechanism of an auditory device, such as one ormore electrodes 112, 114 of a cochlear implant or a speaker on a hearingaid device. The auditory input sensor 104 may be a microphone positionedon the auditory device. The system also includes a database 123 ofreference sound profiles and a plurality of sets of parameter settings,each of which is paired with a corresponding sound profile. The database123 may be stored directly on the auditory device 192 or remotely on thepatient's mobile device or on a remote server.

The auditory device 102 also includes a memory 120 in communication withthe processor 108 and including instructions that, when executed by theprocessor, cause the processor 108 to undertake certain steps that matchthe environmental sound detected by the auditory input sensor 104 withthe reference bank of sounds 123 to identify a comparable environmenthaving associated parameter settings.

More specifically, the processor 108 first receives the environmentalsound detected by the auditory input sensor 104 and analyzes a frequencycontent of the environmental sound. The system may determine thefrequency content of the environmental sound by using a waveletscattering transform to analyze the frequency content of theenvironmental sound, using a Fourier transform to compute the frequencycontent of the environmental sound, or any other suitable classifieralgorithm to determine the frequency content of the acousticenvironment.

The processor 108 compares the frequency content of the environmentalsound with the sound profiles stored in the database 123. In response tothe comparison, the system selects one of the sound profiles andautomatically adjusts the parameter settings of the auditory deviceoutput mechanism, such as electrodes 112, 114, to match the set ofparameter settings associated with the selected sound profile. Each setof the plurality of sets of parameter settings may include amplificationsettings, compression settings, and directional noise rejectionsettings.

FIG. 9 is an example of the clustering 900 of different sound profiles902 based on a plurality of variable parameters 904 to create a databaseor reference bank of environments and corresponding parameters 906.Example sound profiles of environments include the beach, the bus, thecity, the forest, the office, a specific person's voice, such as aparent or child, or other easily characterized or recognizableenvironments or sounds. The database also includes a set of parametersettings associated with each sound profile. The database may alsoinclude parameter settings associated with combinations of recognizedenvironmental sounds, such as, for example, the recognition of aspouse's voice in a home living room environment, which may be adifferent setting than a child's voice in the same living room setting,which may be different from either of the voices in a dining roomsetting. During use, the system determines one or more properties of thefrequency content of the environmental sound such as, but not limitedto, a signal-to-noise ratio, an amplitude range, and a pitch range torecognize the appropriate saved parameters to apply to the system. Whileonly three variable parameters 904 are illustrated in FIG. 9, theclustering software may use many more than three or as few as oneparameter to cluster the sounds by similarity.

Each sound profile may also be associated with a stored geolocation. Alocation sensing mechanism in communication with the auditory deviceprocessor determines the present geolocation of the auditory device.After the system selects a sound profile that corresponds to theenvironmental sound, the processor may further compare a presentgeolocation of the auditory device output mechanism identified by thelocation sensing mechanism with the stored geolocations. The geolocationmay identify a subset sound profile with an associated set of parametersettings. The geolocation may be particularly useful in maintainingconsistency in settings as there are times the positional location willbe more stable than the sound environment. As such, it may be the casethat based on a given geolocation, the processor is instructed to onlychoose between a limited number of settings. For example, in the“office” geolocation, the processor may be restricted to choosingbetween the (i) office desk, (ii) office conference room, and (iii)office cafeteria, settings. A more complex application may includerecognizing the geolocation (for example, the user's home), which limitsthe possible sound profiles from which to choose, then recognizes thebackground noise (for example, the user's living room with thetelevision on) then recognizes the user's spouse's voice to apply asound profile matching settings optimized for the user to hear theuser's spouse in the user's living room with the television on in thebackground.

If the acoustic environment received by the microphone does notcorrespond closely with any of the reference bank of sounds 906, a newenvironmental setting may be created. In one embodiment, the patientcould update his or her parameter preferences for the new acousticenvironment either through the hearing device itself or using a mobileapplication associated with the optimization system of the presentapplication, either through a phone or tablet connected to his or herhearing aid or cochlear implant. In some embodiments, the first andsecond modules are accessible by the patient through a mobileapplication on a mobile device. The patient can use the mobileapplication to tune the parameters to the present environment and storethe set of parameter settings associated with the specific environmentalsound profile in the database of sound profiles 906.

The patient may also add to the reference bank of sounds associated withspecific parameter settings by simulating the sounds during thepatient's visit to an audiologist. For example, an audiologist wouldplace a hearing-impaired user in a sound booth and play speech-in-noiseor speech-in-babble or even more specific acoustic environments, such asspeech on an airplane or speech-in-wind. Using the second module of theoptimization system, the patient sets his or her preferred parameters.When the patient is in the real-world environment, all parametersettings are updated based on the current environment's similarity tothe previously simulated environments.

The foregoing description merely illustrates the principles of thedisclosure. Various modifications and alterations to the describedembodiments will be apparent to those skilled in the art in view of theteachings herein. It will thus be appreciated that those skilled in theart will be able to devise numerous systems, arrangements and methodswhich, although not explicitly shown or described herein, embody theprinciples of the disclosure and are thus within the spirit and scope ofthe present disclosure. From the above description and drawings, it willbe understood by those of ordinary skill in the art that the particularembodiments shown and described are for purposes of illustrations onlyand are not intended to limit the scope of the present disclosure.References to details of particular embodiments are not intended tolimit the scope of the disclosure.

We claim:
 1. A system for controlling parameter settings of an auditorydevice comprising: an auditory device processor; an auditory deviceoutput mechanism controlled by the auditory device processor, theauditory device output mechanism including one or more modifiableparameter settings; an auditory input sensor that detects anenvironmental sound and communicates with the auditory device processor;a database in communication with the auditory device processor, thedatabase pairing each of a plurality of sets of parameter settings witha corresponding sound profile; a memory in communication with theauditory device processor and including instructions that, when executedby the auditory device processor, cause the auditory device processorto: receive the environmental sound detected by the auditory inputsensor; analyze a frequency content of the environmental sound; comparethe frequency content of the environmental sound with the sound profilesstored in the database and, in response to the comparison, select one ofthe sound profiles; and automatically adjust the parameter settings ofthe auditory device output mechanism to match the set of parametersettings associated with the selected sound profile.
 2. The system ofclaim 1, wherein when the auditory device processor analyzes a frequencycontent of the environmental sound it uses a wavelet scatteringtransform to analyze the frequency content of the environmental sound.3. The system of claim 1, wherein when the auditory device processoranalyzes a frequency content of the environmental sound it uses aFourier transform to compute the frequency content of the environmentalsound.
 4. The system of claim 1, wherein the sound profiles areclustered by similarities.
 5. The system of claim 1, wherein thefrequency content of the environmental sound includes one or moreproperties selected from the group comprising a signal-to-noise ratio,an amplitude range, and a pitch range.
 6. The system of claim 6, whereinone or more properties selected from the group comprising asignal-to-noise ratio, an amplitude range, and a pitch range of thefrequency content of the environmental sound matches the correspondingone or more properties selected from the group comprising asignal-to-noise ratio, an amplitude range, and a pitch range of one ofthe sound profiles.
 7. The system of claim 1 wherein the auditory deviceoutput mechanism is an electrode of a cochlear implant.
 8. The system ofclaim 1 wherein the auditory device output mechanism is speaker of ahearing aid.
 9. The system of claim 1 wherein the auditory input sensoris microphone of a cochlear implant.
 10. The system of claim 1 whereinthe auditory input sensor is microphone of a hearing aid.
 11. The systemof claim 1 wherein each set of the plurality of sets of parametersettings includes amplification settings, compression settings, anddirectional noise rejection settings.
 12. The system of claim 1 whereineach sound profile is associated with a stored geolocation, the systemfurther comprises a location sensing mechanism in communication with theauditory device processor, and when the processor compares the frequencycontent of the environmental sound with the sound profiles stored in thedatabase and, in response to the comparison, selects one of the soundprofiles, the processor further compares a present geolocation of theauditory device output mechanism identified by the location sensingmechanism with the stored geolocations.
 13. A system for controllingparameter settings of an auditory device comprising: an auditory deviceprocessor; an auditory device output mechanism controlled by theauditory device processor, the auditory device output mechanismincluding one or more modifiable parameter settings; an auditory inputsensor that detects an environmental sound and communicates with theauditory device processor; a database in communication with the auditorydevice processor, the database pairing a first set of parameter settingswith a corresponding first sound profile and a second set of parametersettings with a corresponding second sound profile; a memory incommunication with the auditory device processor and includinginstructions that, when executed by the auditory device processor, causethe auditory device processor to: receive the environmental sounddetected by the auditory input sensor; analyze a frequency content ofthe environmental sound; compare the frequency content of theenvironmental sound with the first and second sound profiles stored inthe database and, in response to the comparison, select one of the firstand second sound profiles; and automatically adjust the parametersettings of the auditory device output mechanism to match the set ofparameter settings corresponding with the selected first or second soundprofile.
 14. The system of claim 13, wherein when the auditory deviceprocessor analyzes a frequency content of the environmental sound ituses a wavelet scattering transform to analyze the frequency content ofthe environmental sound.
 15. The system of claim 13, wherein when theauditory device processor analyzes a frequency content of theenvironmental sound it uses a Fourier transform to compute the frequencycontent of the environmental sound.
 16. The system of claim 13, whereinthe sound profiles are clustered by similarities.
 17. The system ofclaim 13, wherein the frequency content of the environmental soundincludes one or more properties selected from the group comprising asignal-to-noise ratio, an amplitude range, and a pitch range.
 18. Thesystem of claim 17, wherein one or more properties selected from thegroup comprising a signal-to-noise ratio, an amplitude range, and apitch range of the frequency content of the environmental sound matchesthe corresponding one or more properties selected from the groupcomprising a signal-to-noise ratio, an amplitude range, and a pitchrange of one of the sound profiles.
 18. The system of claim 13 whereinthe auditory device output mechanism is an electrode of a cochlearimplant and the auditory input sensor is microphone of a cochlearimplant.
 19. The system of claim 13 wherein the auditory device outputmechanism is speaker of a hearing aid and the auditory input sensor ismicrophone of a hearing aid.
 20. The system of claim 13 wherein each setof the plurality of sets of parameter settings includes amplificationsettings, compression settings, and directional noise rejectionsettings.